Experiment 1 - analysis delta RTs
Didino Daniele
October 2021
This file is part of the supplementary material of the manuscript: Didino, D., Brandtner, M., & Knops, A. (2021). No influence of masked priming on the multiplication fact retrieval in a result verification task.
This script reports the analysis for experiment 1.
Dataset loaded: exp1_data.rds (data of result verification task)
Load libraries, my functions (my_functions folder) and data (data/processed folder):
library('ggpubr')
#> Loading required package: ggplot2
library('here')
#> here() starts at D:/mult_prime
library('kableExtra')
# library('knitr')
library('plotly')
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#> filter
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#> layout
library('ggridges')
library('tidyverse')
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library('BayesFactor')
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#> Welcome to BayesFactor 0.9.12-4.2. If you have questions, please contact Richard Morey (richarddmorey@gmail.com).
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# Load my functions
source(here('funcs', 'load_my_functions.R'))
# Load data
exp1 <- readRDS(here('data', 'processed', 'exp1_data.rds'))Exclude outliers
exp1 <-
exp1 %>%
filter(outlier == FALSE)Calculate delta RTs
exp1_dRT <-
exp1 %>%
return_delta_RT(.,
y_col = RT,
cols_to_group = c('sj', 'problem_size'),
cols_to_subtract = c('prime_cond'),
neutral_cond = 'neutral')
#> `summarise()` has grouped output by 'sj', 'problem_size'. You can override using the `.groups` argument.Delta RTs versus RTs
dens_RT <-
ggplot(exp1, aes(RT)) +
geom_density() +
stat_function(fun = dnorm,
args = list(mean = mean(exp1$RT), sd = sd(exp1$RT)),
color = 'blue') +
ggtitle('')
dens_dRT <-
ggplot(exp1_dRT, aes(dRT)) +
geom_density() +
stat_function(fun = dnorm,
args = list(mean = mean(exp1_dRT$dRT), sd = sd(exp1_dRT$dRT)),
color = 'blue') +
ggtitle('')
ggarrange(dens_RT,
dens_dRT,
ncol = 2,
nrow = 1,
labels = c('RT', 'delta RT'))
Delta RTs table
Table with RTs mean, standard deviation and standard error (aggregated on subject and condition):
# Calculate statistics
exp1_stats <-
exp1_dRT %>%
return_stats(c('problem_size', 'cond'),
DV = 'dRT')
#> `summarise()` has grouped output by 'sj', 'problem_size'. You can override using the `.groups` argument.
#> `summarise()` has grouped output by 'problem_size'. You can override using the `.groups` argument.
# Show results
exp1_stats %>%
make_table('Statistics (prime condition by problem size)')| problem_size | cond | N_sj | Mean | SD | SE |
|---|---|---|---|---|---|
| large | identity | 28 | -42.73 | 53.06 | 10.03 |
| large | neigh_con | 28 | 25.05 | 52.21 | 9.87 |
| large | neigh_inc | 28 | 47.45 | 45.98 | 8.69 |
| large | unrel_con | 28 | 5.33 | 55.49 | 10.49 |
| large | unrel_inc | 28 | 48.06 | 63.47 | 11.99 |
| small | identity | 28 | -37.17 | 44.19 | 8.35 |
| small | neigh_con | 28 | 22.58 | 55.73 | 10.53 |
| small | neigh_inc | 28 | 23.96 | 45.06 | 8.52 |
| small | unrel_con | 28 | 49.62 | 56.39 | 10.66 |
| small | unrel_inc | 28 | 39.83 | 51.70 | 9.77 |
Delta RTs plots
RTs distribution across prime conditions:
plot_1 <-
exp1_dRT %>%
ggplot(aes(dRT, fill = cond)) +
geom_density(alpha = 0.6) +
facet_wrap(~ problem_size, ncol = 2)
ggplotly(plot_1)exp1_dRT %>%
ggplot(aes(x = dRT, y = cond, fill = stat(x))) +
geom_density_ridges_gradient(scale = 3) +
scale_fill_viridis_c(name = 'RTs', option = 'C') +
labs(y = 'Prime condition') +
facet_wrap(~ problem_size, ncol = 2)
#> Picking joint bandwidth of 23.9
#> Picking joint bandwidth of 22.2
Bayes factor
Bayes factors are computer with the package BayesFactor.
tribble(
~BF, ~Evidence,
'0-3', 'Anectodal',
'3-10', 'Moderate',
'10-30', 'Strong',
'30-100', 'Very_strong',
'>100', 'Decisive') %>%
make_table('Bayes factor interpretation')| BF | Evidence |
|---|---|
| 0-3 | Anectodal |
| 3-10 | Moderate |
| 10-30 | Strong |
| 30-100 | Very_strong |
| >100 | Decisive |
exp1_bf <-
exp1_dRT %>%
group_by(problem_size, cond) %>%
nest() %>%
mutate(
# "DV" is the column with the means returned by "return_means()"
BF_test = map(data,
~ttestBF(x = .$dRT,
mu = 0)),
BF_value = map(BF_test,
~extractBF(.) %>%
tibble %>%
select(bf, error))
) %>%
unnest(BF_value)
exp1_bf
#> # A tibble: 10 x 6
#> # Groups: problem_size, cond [10]
#> problem_size cond data BF_test bf error
#> <chr> <chr> <list> <list> <dbl> <dbl>
#> 1 large identity <tibble[,2] [28 x 2]> <BFBysFct> 129. 0.0000000985
#> 2 large neigh_con <tibble[,2] [28 x 2]> <BFBysFct> 2.93 0.0000000914
#> 3 large neigh_inc <tibble[,2] [28 x 2]> <BFBysFct> 2388. 0.0000000204
#> 4 large unrel_con <tibble[,2] [28 x 2]> <BFBysFct> 0.226 0.0000779
#> 5 large unrel_inc <tibble[,2] [28 x 2]> <BFBysFct> 70.5 0.0000000384
#> 6 small identity <tibble[,2] [28 x 2]> <BFBysFct> 203. 0.000000107
#> 7 small neigh_con <tibble[,2] [28 x 2]> <BFBysFct> 1.43 0.000000112
#> 8 small neigh_inc <tibble[,2] [28 x 2]> <BFBysFct> 5.03 0.0000000798
#> 9 small unrel_con <tibble[,2] [28 x 2]> <BFBysFct> 334. 0.0000000914
#> 10 small unrel_inc <tibble[,2] [28 x 2]> <BFBysFct> 83.2 0.0000000561
exp1_bf <-
exp1_bf %>%
select(problem_size, cond, bf) %>%
rename(BF_10 = bf) %>%
mutate(
BF_01 = 1 / BF_10
)
# Show results
exp1_bf %>%
make_table('Bayes factor')| problem_size | cond | BF_10 | BF_01 |
|---|---|---|---|
| large | identity | 129.00 | 0.01 |
| large | neigh_con | 2.93 | 0.34 |
| large | neigh_inc | 2388.08 | 0.00 |
| large | unrel_con | 0.23 | 4.43 |
| large | unrel_inc | 70.51 | 0.01 |
| small | identity | 203.26 | 0.00 |
| small | neigh_con | 1.43 | 0.70 |
| small | neigh_inc | 5.03 | 0.20 |
| small | unrel_con | 334.14 | 0.00 |
| small | unrel_inc | 83.18 | 0.01 |
Plot dRT against mean RT (by subject)
Create a single data frame with dRT and mean RT (a data point for each subject, problem size and prime condition)
# Mean RT by subject
exp1_meanRT_sj <-
exp1 %>%
return_stats(c('sj', 'problem_size', 'cond'),
DV = 'RT') %>%
select(sj, problem_size, prime_cond, Mean) %>%
rename(cond = prime_cond) %>%
filter(cond != 'neutral')
#> `summarise()` has grouped output by 'sj', 'problem_size'. You can override using the `.groups` argument.
#> `summarise()` has grouped output by 'sj', 'problem_size'. You can override using the `.groups` argument.
# Join data frames
exp1_dRT_meanRT <-
full_join(exp1_dRT,
exp1_meanRT_sj,
by = c('sj', 'problem_size', 'cond'))Plot for large problems
exp1_dRT_meanRT %>%
filter(problem_size == 'large') %>%
ggplot(aes(Mean, dRT)) +
geom_point() +
geom_hline(yintercept = 0,
linetype = 'dashed',
color = 'red',
size = 0.5) +
facet_wrap(~ cond)
Plot for small problems
exp1_dRT_meanRT %>%
filter(problem_size == 'small') %>%
ggplot(aes(Mean, dRT)) +
geom_point() +
geom_hline(yintercept = 0,
linetype = 'dashed',
color = 'red',
size = 0.5) +
facet_wrap(~ cond)
session information
xfun::session_info()
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